University of Oulu

Yusuke Koda, Jihong Park, Mehdi Bennis, Koji Yamamoto, Takayuki Nishio, and Masahiro Morikura. 2019. One Pixel Image and RF Signal Based Split Learning for mmWave Received Power Prediction. In Proceedings of the 15th International Conference on emerging Networking EXperiments and Technologies (CoNEXT ’19). Association for Computing Machinery, New York, NY, USA, 54–56. DOI:

One pixel image and RF signal based split learning for mmwave received power prediction

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Author: Koda, Yusuke1; Park, Jihong2; Bennis, Mehdi2;
Organizations: 1Graduate School of Informatics, Kyoto University, Kyoto, Japan
2Centre for Wireless Communication, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 5.2 MB)
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Language: English
Published: Association for Computing Machinery, 2019
Publish Date: 2020-05-04


Focusing on the received power prediction of millimeter-wave (mmWave) radio-frequency (RF) signals, we propose a multimodal split learning (SL) framework that integrates RF received signal powers and depth-images observed by physically separated entities. To improve its communication efficiency while preserving data privacy, we propose an SL neural network architecture that compresses the communication payload, i.e., images. Compared to a baseline solely utilizing RF signals, numerical results show that SL integrating only one pixel image with RF signals achieves higher prediction accuracy while maximizing both communication efficiency and privacy guarantees.

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ISBN Print: 978-1-4503-7006-6
Pages: 54 - 56
DOI: 10.1145/3360468.3368176
Host publication: 15th International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2019 - Part of CoNEXT 2019
Conference: International Conference on Emerging Networking EXperiments and Technologies
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: The author would like to thank Mr. Kota Nakashima for providing the data set. This work was supported in part by JSPS KAKENHI Grant Numbers JP17H03266, JP18H01442, and KDDI Foundation. This work was also supported in part by the Academy of Finland under Grant 294128, in part by the 6Genesis Flagship under Grant 318927, in part by the KvantumInstitute Strategic Project (SAFARI), in part by the Academy of Finland through the MISSION Project under Grant 319759, and in part by the Artificial Intelligence for Mobile Wireless Systems (AIMS) project at the University of Oulu
Academy of Finland Grant Number: 294128
Detailed Information: 294128 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
319759 (Academy of Finland Funding decision)
Copyright information: © 2019 Copyright held by the owner/author(s). This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 15th International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2019 - Part of CoNEXT 2019,